Physiological monitor with mobile computing device connectivity

ABSTRACT

Systems and method for monitoring patient physiological data are presented herein. In one embodiment, a physiological sensor and a mobile computing device can be connected via a cable or cables, and a processing board can be connected between the sensor and the mobile computing device to conduct advanced signal processing on the data received from the sensor before the data is transmitted for display on the mobile computing device.

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.61/703,729 filed Sep. 20, 2012, entitled “Patient Monitor with MobileComputing Device Connectivity,” the disclosure of which is herebyincorporated by reference in its entirety.

BACKGROUND

Field of the Disclosure

The present disclosure relates in general to noninvasive patientmonitoring systems, including oximeters and co-oximeters, and theiraccessories such as sensors or cables. In particular, this disclosurerelates to patient monitors capable of connectivity to a mobilecomputing device.

Description of the Related Art

Oximetry utilizes a noninvasive optical sensor to measure physiologicalparameters of a patient. In general, the sensor has light emittingdiodes (LEDs) that transmit optical radiation into a tissue site and adetector that responds to the intensity of the optical radiation afterabsorption (e.g., by transmission or transreflectance) by, for example,pulsatile arterial blood flowing within the tissue site. Based on thisresponse, a processor determines measurements for oxygen saturation(SpO₂), pulse rate, plethysmograph waveforms, perfusion quality index(e.g., an index that quantifies perfusion), assessments of other bloodconstituents, parameters or analytes, including for example, a percentvalue for arterial carbon monoxide saturation (HbCO), a percent valuefor methemoglobin saturation (a brownish-red form of hemoglobin thatcannot function as an oxygen carrier) (HbMet), total hemoglobin (HbT),fractional SpO₂ (SpaO₂) or the like. Additionally, caregivers oftendesire knowledge of HbO₂, Hb, blood glucose (HbGu), water, the presenceor absence of therapeutic drugs (aspirin, Dapson, nitrates, or the like)or abusive/recreational drugs (methamphetamine, alcohol, steroids, orthe like), concentrations of carbon dioxide (CO₂), oxygen (O₂), oxygenconcentration, pH levels, bilirubin, perfusion quality, albumin,cyanmethemoglobin, and sulfhemoglobin (HbSulf), signal quality or thelike. It is noted that “oximetry” as used herein encompasses its broadordinary meaning known to one of skill in the art, which includes atleast those noninvasive procedures for measuring parameters ofcirculating blood through spectroscopy. Moreover, “plethysmograph” asused herein (commonly referred to as “photoplethysmograph”), encompassesits broad ordinary meaning known to one of skill in the art, whichincludes at least data representative of a change in the absorption ofparticular wavelengths of light as a function of the changes in bodytissue resulting from pulsing blood.

Oximeters capable of reading many of the foregoing parameters duringnoise due to patient movement, electromagnetic interference, and ambientlight are available from Masimo Corporation (Masimo) of Irvine, Calif.Moreover, portable and other oximeters are disclosed in at least U.S.Pat. Nos. 6,770,028, 6,658,276, 6,157,850, 6,002,952, and 5,769,785,incorporated by reference herein, and others patent publications such asthose listed at http://www.masimo.com/patents.htm. Such noise filteringoximeters have gained rapid acceptance in a wide variety of medicalapplications, including surgical wards, intensive care and neonatalunits, general wards, home care, physical training, and virtually alltypes of monitoring scenarios. Some blood parameter monitors includingoximeters are the standard of care in certain critical environments likesurgery and neonatal care.

SUMMARY

Mobility and ease of use are key factors in the health care industrybecause they correlate to efficient, rapid patient care as well asenable patients to participate in their own care. Therefore, the presentdisclosure provides physiological monitoring devices which arecompatible with handheld monitors such as common mobile computingdevices for ease of use and portability.

This disclosure describes embodiments of a mobile physiological sensorthat can be conveniently used in conjunction with existing mobiledevices of users in a variety of contexts. In certain embodiments, aphysiological monitoring system can be designed to include a sensor andcable assembly with a processing board or card, and the system can beconnectable to a mobile computing device, such as a smartphone, suchthat display of the monitored physiological data can occur on thecomputing device. The board or card can communicate the data for displaywith the mobile computing device wirelessly or through a physical andelectrical connection with the cable assembly. In some embodiments, theboard or card can include one or more signal processors and associatedmemory, I/O, and the like to provide monitored physiological data toapplications executing on traditional smartphone processingenvironments, such that board or card handles advanced signal processingand the smartphone displays parameter data. In an embodiment, the boardis housed in a portion of the cable such that it is not directly coupledto the sensor or the smartphone connector. This configuration has theadvantage of mechanically isolating the board so that it does notencumber the sensor or the smart phone connection. As a result, thephysiological monitoring system can be more portable than existingmonitoring systems, thereby facilitating enhanced patient care for morepatients.

For example, such a system can be sent home with a patient to gatherphysiological measurement data outside the hospital setting. Inaddition, portable physiological monitoring equipment as disclosedherein can facilitate the gathering of physiological measurement data ina variety of other contexts, such as sports or extreme sports, militarytraining and combat, aviation, health awareness, high-altitudeactivities, monitoring of professionals in dangerous conditions,screening for medical conditions such as congenital heart defects, fieldhospitals, and mobile medical clinics, to name a few.

Physiological monitoring systems such as those that are described hereinenable oximeter use outside of the traditional hospital setting. This isbeneficial for more comprehensive patient care. For instance, prior to asurgical procedure during which a patient will be sedated, such as bygeneral anesthesia, a physician can be concerned about the patient'sproclivity toward apnea. A portable oximetry sensor compatible with thepatient's smartphone can be sent home with the patient prior to theprocedure, and the sensor can be worn overnight. Data collected from thesensor can be passed to the smartphone and made available to the doctor,such as by uploading to the internet or being downloadable from thedevice, to identify a risk of hypoxemia. This example illustrates one ofthe many benefits of a portable oximetry system compatible with a commonmobile computing device.

For purposes of summarizing the disclosure, certain aspects, advantagesand novel features of the inventions have been described herein. It isto be understood that not necessarily all such advantages can beachieved in accordance with any particular embodiment of the inventionsdisclosed herein. Thus, the inventions disclosed herein can be embodiedor carried out in a manner that achieves or optimizes one advantage orgroup of advantages as taught herein without necessarily achieving otheradvantages as can be taught or suggested herein.

BRIEF DESCRIPTION OF THE DRAWINGS

Throughout the drawings, reference numbers can be re-used to indicatecorrespondence between referenced elements. The drawings are provided toillustrate embodiments of the inventions described herein and not tolimit the scope thereof.

FIG. 1A illustrates an embodiment of a physiological monitoring system.

FIG. 1B illustrates another embodiment of a physiological monitoringsystem.

FIG. 1C illustrates an exploded view of one embodiment of the cablecomponents of FIG. 1A.

FIG. 2 illustrates a block diagram of an embodiment of a mobilephysiological monitoring system.

FIG. 3 illustrates an embodiment of a computing environment in which amobile patient monitoring device can communicate with various computingdevices and services over a network.

FIGS. 4A-4D illustrate various embodiments of software applications fordisplay and management of physiological monitoring data.

FIGS. 5A-5C illustrate various embodiments of mobile physiologicalsensors assemblies.

FIG. 6 illustrates an embodiment of a pre-anesthesia monitoring process.

FIG. 7 illustrates an embodiment of a continuum of care process.

FIG. 8 illustrates an embodiment of a mobile physiological datamonitoring process.

FIG. 9 illustrates an embodiment of a user-guided monitoring process.

FIG. 10 illustrates an embodiment of a data-logging process.

DETAILED DESCRIPTION I. Example Mobile Physiological Monitoring Systems

FIGS. 1A, 1B, and 1C illustrate embodiments of a physiologicalmonitoring system 100. The physiological monitoring system 100 shown inFIG. 1A includes a sensor 110, first cable 120, processing module 130,second cable 140, connection port 150, and a mobile computing device,illustrated here as smartphone 160. Although specific reference can bemade to smartphones in this disclosure, any mobile computing devicecompatible with the physiological sensor system can be used. Acompatible mobile computing device can be one of a wide range of mobiledevices such as a mobile communications device (such as a smartphone),laptop, tablet computer, netbook, PDA, media player, mobile gameconsole, wristwatch, wearable computing device, or other microprocessorbased device configured to interface with a physiological sensor. Someembodiments of the mobile computing device can be used with the systemfor display of data and/or storage of data. Cables 120, 140 used withthe device can be flex cables or other cables, including cables havingtriboelectric properties.

As illustrated, the sensor 110 can be a pulse oximeter capable of beingsecured to a digit such as a finger, for example the Masimo Rainbow®pulse oximeter. However, this is for illustrative purposes only, and thesensor 110 can be any physiological sensor. In some embodiments, othervarieties of pulse oximeters can be used, for example adhesive sensors,combination reusable/disposable sensors, soft and/or flexible wrapsensors, infant or pediatric sensors, multisite sensors, or sensorsshaped for measurement at a tissue site such as an ear. In otherembodiments, the sensor 110 can be any of a variety of sensors, such asa pulse oximeter, a brain function monitor such as anelectroencephalograph (“EEG”), a gas monitor such as a capnometer orcapnograph, an acoustic respiratory sensor, a heart function monitorsuch as an electrocardiograph (“ECG”), blood alcohol level sensors,temperature sensors, respiratory inductive plethysmography bands,bioelectric sensors, electronic fetal monitors, or the like. The sensor110 can be reusable in some embodiments, can be disposable in someembodiments, and in other embodiments the sensor 110 can have bothreusable and disposable components. In some embodiments, the sensor canbe available in different sizes.

As illustrated in FIG. 1B, in an embodiment, cable 120 can include aport 170 at the sensor-facing end of the cable 120, and a disposable,connectable sensor 180 may be attached to the cable 120. In someembodiments, the connectable sensor 180 can be reusable, or can bepartially reusable and partially disposable. A sensor connectionmechanism 172 can be configured to receive, or otherwise connect to,connectable sensors of different types, such as any of the physiologicalsensors discussed above. Although connection port 150 is illustrated asbeing configured for physical and electrical connection to a mobiledevice, in some embodiments, the connection port may be a wirelessconnection port configured to wirelessly transmit filtered physiologicalparameter data to the mobile device or another computing device.

In various oximeter embodiments, the sensor 110 provides data in theform of an output signal indicative of an amount of attenuation ofpredetermined wavelengths (ranges of wavelengths) of light by bodytissues, such as, for example, a digit, portions of the nose or ear, afoot, or the like. The predetermined wavelengths often correspond tospecific physiological parameter data desired, including for example,blood oxygen information such as oxygen content (“SpOC”), oxygensaturation (“SpO2”), blood glucose, total hemoglobin (“SbHb”),methemoglobin (SbMet”), carboxyhemoglobin (“SpCO”), bulk tissue propertymeasurements, water content, pH, blood pressure, respiration relatedinformation, cardiac information, indications of perfusion (“PI”), plethvariability indices (“PVI”), or the like. In some embodiments, sensordata can provide information regarding physiological parameters such asEEG, ECG, acoustic respiration rate (“RRa”), end-tidal carbon dioxide(“EtCO₂”), return of spontaneous circulation (“ROSC”), or the like.

The sensor data can be corrupted by noise due to patient movement,electromagnetic interference, or ambient light. Therefore, the sensordata is transmitted from sensor 110 along the first cable 120 to theprocessing module 130, which can apply noise filtering and signalprocessing techniques described below to provide output data for displayon the smartphone 160. Such complex processing techniques can exceed theprocessing capabilities of the smartphone 160, and therefore theprocessing module 130 drives operation of the sensor 110 and handlessignal processing and transmits the processed sensor parameter data asoutput measurement data. Smartphone 160 can be coupled to the processingmodule 130 by a second cable 140 and connection port 150, in someembodiments, and in other embodiments can be configured to wirelesslytransmit the parameter data to the smartphone 160 or another computingdevice.

Smartphone 160 can include a display screen such as an LED or LCDscreen, and can include touch sensitive technologies in combination withthe display screen. Smartphone 160 can include software configured todisplay some or all of the output measurement data on the displayscreen. The data display can include numerical or graphicalrepresentations of blood oxygen saturation, heart rate, and/or aplethysmographic waveform, and some embodiments can simultaneouslydisplay numerical and graphical data representations.

The smartphone 160 can include software such as an applicationconfigured to manage output measurement data from the processing module130. The application functionality can include trend analysis, currentmeasurement information, alarms associated with above/below thresholdreadings, reminders to take measurement data at certain times or cycles,display customization, iconic data such as hearts beating, colorcoordination, bar graphs, gas bars, charts, graphs, or the like, allusable by a caregiver or smartphone user to enable helpful and directedmedical monitoring of specified physiological parameters. The smartphone160 can also include network connection capabilities such as one or moreof a cellular network, satellite network, Bluetooth, ZigBee, wirelessnetwork connection such as Wi-Fi, and a wired network connection.

In some embodiments, software capable of analyzing the outputmeasurement data received from the processing module 130 and making thedata available in an appropriate manner for health management isinstalled on the smartphone 160. In some embodiments, the smartphone 160includes software which allows a user to view the data in a multitude ofways. For example, in some embodiments a user can be able to view theraw data received from the sensor 110. In other embodiments, a user canbe able to select from a plurality of graphical representations of thedata (e.g., bar graphs, charts, etc). In other embodiments, the user canbe able to manipulate the data to visualize trends in the data. Thesmartphone 160 can also be able to alert the user and/or a physician orother care provider to an abnormal data reading. For example, anabnormally low or high blood oxygen saturation reading can cause thesmartphone 160 to buzz, vibrate or otherwise notify the user of anabnormal reading, or to transmit a notification to a physician via anetwork.

The smartphone 160 can have the capability of sending physiological datato a computer (e.g., a home computer) on which the user manages hishealth data. The data can also be sent to a physician or pharmacist fortheir expertise and feedback. The smartphone 160 and the computingdevice to which data is being sent can be connected directly or via anetwork such as a LAN, WAN or the Internet. The connection can be wiredor wireless. Other connection configurations are also possible.

The system 100 as illustrated in FIG. 1C shows an exploded view of theprocessing module 130 and the connection port 150 to reveal thecomponents thereof. The processing module 130 drives operation of thesensor 110 and receives raw detected signals from the sensor 110. Theprocessing module 130 processes the raw detected signals to determine aphysiological measurement. The processing module 130, in someembodiments, employs advanced signal processing techniques, includingparallel engines and adaptive filters, to allow accurate monitoring ofarterial oxygen saturation and pulse rate even during the mostchallenging conditions. In some embodiments, the processing module 130can employ Signal Extraction Technology, or Masimo SET®, using parallelsignal processing engines to separate the arterial signal from sourcesof noise (including the venous signal) to measure SpO₂ and pulse rateaccurately, even during motion. The processing module 130 can filter rawphysiological sensor data input from the sensor 110, and the processingmodule 130 can provide filtered physiological parameter data to themobile computing device for display or storage.

One drawback of implementing physiological measurement technology onmobile computing devices is the processing overhead typically requiredfor recognizing parameters from data input by the sensor by filteringsuch raw physiological measurement data. Processing overhead measuresthe total amount of work the central processing unit (CPU) of the devicecan perform and the percentage of that total capacity which is used byindividual computing tasks, such as filtering raw physiologicalmeasurement data. In total, these tasks must require less than theprocessor's overall capacity. Moreover, complicated software required toprocess raw signals and determine physiological measurements can bestored in the processing module 130 in a separate memory unit separatefrom the mobile device. This frees up memory available to the mobiledevice.

The current generation of mobile processors is not well adapted to dealwith the complexity and corresponding processing overhead of filteringraw physiological measurement data, especially in conjunction with themany other common high performance uses of mobile devices. As anexample, the mobile device processor may be used to run a mobilephysiological monitoring application concurrently with receiving sensordata, among other applications selected by the user. Many common mobileapplications such as maps, games, email clients, web browsers, etc., aretypically open on a user's smartphone. During physiological monitoring,a substantially constant stream of data can be sent from the sensor tothe mobile device. Accordingly, if the mobile CPU is required to filterthe raw data, device performance can be impaired and the user canexperience significant latency in the use of other applications. If thedata filtering overhead exceeds the overall processing capacity of theCPU then the mobile device would be incapable of processing the data,and the user can experience serious technical problems as a result.

Overload of the CPU can significantly increase system power consumption.To mitigate the possibility of CPU overload, a larger processor can beprovided. However, increasing the size of the mobile processor core orcache would deliver performance increases only up to a certain level,beyond which heat dissipation issues would make any further increase incore and cache size impractical. Additionally, overall processingcapacity is further limited by the smaller size of many mobile devices,which limits the number of processors that can be included in thedevice. Because mobile computing devices are generally battery-powered,high performance uses also shortens battery life.

By providing a separate processing module 130 to mediate the data flowfrom the sensor 110 to the mobile device 160, the complex signalprocessing required for generating recognizable physiological parametersfrom raw sensor data can be handled by the processing module 130 and notthe mobile CPU. Moving the signal processing calculations away from themobile CPU frees it up for important core tasks as well as processing ofmobile applications. Further, optimizing the mobile CPU can directlycorrelate with increased battery life, even considering the power drawof the processing module 130 on the mobile device battery. Accordingly,incorporation of a processing module 130 into a mobile sensor cable canbe beneficial for conserving processing of the mobile CPU and forreducing battery demands across the system 100.

Coupled to cable 120 is an information element 133. The informationelement 133 could be provided through an active circuit such as atransistor network, memory chip, EEPROM (electronically erasableprogrammable read-only memory), EPROM (erasable programmable read-onlymemory), or other identification device, such as multi-contact singlewire memory devices or other devices, or the like.

The processing module 130 includes a lower shell 131, an enclosure withbend relief 132, processing board 134, and an upper shell 135. Theenclosure 132, upper shell 135, and lower shell 131 surround theprocessing board 134 and can protect the sensitive circuitry of theboard 134 from damage. In such an embodiment, processing board 134 isthe portion of the module 130 that communicates with the first cable 120and sensor 110, as well as with the second cable 140 and mobilecomputing device. In an embodiment, the board 134 can access informationstored on the information element 133 of the first cable 120.

In an embodiment, the processing module 130 is located in a middleportion of the cable, away from either the sensor 110 or the connectionport 150. The processing module 130 can be located a first distancefrom, and mechanically isolated from, the sensor, so as not to interferewith the placement of the sensor on a measurement site of a user's body.This placement prevents the sensor from being encumbered by theprocessing module 130 and interfering with placement and use of thesensor. Thus, the sensor is also kept relatively lightweight for ease ofuse. The processing module 130 can be located a first distance from, andmechanically isolated from, the connection port 150, so as not tointerfere with the ability of the connection port 150 to secure to auser's mobile device. This allows the connection port 150 to beunencumbered by the bulk and weight of the processing module 130 whichcould interfere with the connection to the user's mobile device. In someembodiments, the second distance can be smaller than the first distance,placing the processing module 130 closer to the connection port 150 thanto the sensor 110. This prevents the weight of the processing module 130from interfering with or pulling on the sensor 110. In an embodiment,the components of the processing module 130 are constructed fromlightweight materials in order to avoid pulling the sensor 110 off of auser or disconnecting the connection port 150 from a mobile device.

The processing module 130 and sensor 110 draw power for operation fromthe mobile computing device for operation. This frees the processingmodule 130 from needing a separate power source. Also, although adisplay screen can be included on the processing module 130, no separatedisplay screen is necessary as the measurements are displayed on theuser's mobile device.

The enclosure 132 can have a bend relief portion 138 on either side. Thebend relief portions 138 may enhance the electrical and mechanicalintegrity and overall performance of the cable assembly by providing agradual transition from the flexible cables to substantially rigidconnection points with the processing board 134 contained within theenclosure. The bend relief portions 138 can prevent mechanical force,such as an axial load or flexing, that is applied to the exterior ofeither cable 120, 140 from being transferred to the electricalterminations with the processing board 134. The bend relief portions 138can be premolded and formed with the body of the enclosure, and in someembodiments a crimp ring may be secured around the cable within eachbend relief.

The enclosure 132 can be formed, in some embodiments, by a flexibleplastic or rubber material. Suitable materials can include thermoplasticrubbers such as Santoprene®. The upper and lower shells 135, 131 can beformed from a hard plastic material. Suitable materials can includethermoplastic polymers. For example, in an embodiment the upper andlower shells 135, 131 can be formed from a blend of two or more ofpolycarbonate (PC), polyethylene terephthalate (PET), polybutyleneterephthalate (PBT), or another polyester, such as Bayer Makroblend®UT5207. In another embodiment, the upper and lower shells 135, 131 canbe formed from a resin, for example a blend of semi-crystallinepolyester (typically PET or PBT) and PC, such as XENOY™ Resin 6620U. Thematerial for the upper and lower shells 135, 131 can be selected forhaving desirable impact resistance, toughness, and heat resistance. Theupper and lower shells 135, 131 can be formed from the same or differentmaterials.

The body portion of the enclosure 132 can be formed as a gasket whichcan seal between the upper shell 135 and lower shell 131 and form asubstantially water-tight seal, in order to protect the processing board134 from moisture. In some embodiments, the upper and lower shells 135,131 can be formed to fit together with the enclosure 132 in asubstantially water-tight manner. In an embodiment, the upper and lowershells 135, 131 can be sealed to the enclosure 132 using epoxy aroundthe perimeter of each shell, and/or on mounting posts located on theshell or the enclosure. In some embodiments, the cable entry areas ofeach bend relief portion 138 of the enclosure 132 can also be filledwith epoxy to form a substantially sealed enclosure for the processingboard 134.

The cables 120, 140 can be constructed with a Kevlar fiber core forstrength and durability, in some embodiments. The Kevlar fiber core canbe bundled in the center of a plurality of signal lines, for examplefive signal lines. The signal lines can be tinned copper jacketed withpolyprolylene (PP). The bundle of signal lines can be encased in abraided outer shield, for example a tinned copper outer shield withapproximately 95% minimum coverage of the bundled signal lines. Theouter shield may be encased, in turn, by a multi-layer Teflon film orwrap, in some embodiments, to form a low-friction separator and barrierfrom an outer jacket. The cables 120, 140 can be further protected by amedical grade PVC outer jacket, or an outer jacket constructed fromanother biocompatible, flexible plastic or rubber material. Otherconfigurations for the cables 120, 140 are possible. The cables can bedesigned to have a minimum pull strength of 75 kg, or approximately 75kg, in some embodiments.

As illustrated, some embodiments can optionally include a secondprocessing board 136. For example, the first processing board 134 can bea digital processing board and the second processing board 136 can be ananalog processing board. The analog and digital processing boards mayperform separate processing functions. In some embodiments, wires fromthe first cable 120 can be connected to the analog processing board 136,and wires from the second cable 140 can be connected to the digitalprocessing board 136. In some embodiments, the digital processing boardcan be in communication with the first information element 133. Thefirst information element 133 can be an EPROM or EEPROM device. Theanalog processing board can be in communication with a secondinformation element 137 coupled to cable 120. The second informationelement 127 can be a resistor, in some embodiments, for example an ArCalor ProCal resistor. A resistance value of the resistor can be indicativeof a wavelength of light used in an oximetry sensor 110 coupled to thecable 120, and the resistor can be coupled in parallel with the sensor.

In one embodiment, the processing board or boards can include one ofmany OEM boards commercially available from Masimo which processincoming intensity signals responsive to an amount of attenuation oflight in pulsing patient blood and which determine output measurementsfor a wide variety of physiological parameters from the processing. Theprocessing board 134 can include the MS-2040 OEM board available fromMasimo, which can measure Masimo optical SET measurements such as oxygensaturation (SpO₂), pulse rate, perfusion index (PI), signal quality(SIQ), optionally pleth variability index (PVI), and the like. Thephysiological monitoring system 100 can also include, in addition to orinstead of the MS-2040 OEM board, other processing boards available fromMasimo. For example, the physiological monitoring system 100 can includethe MX-5 board available from Masimo, which has variable powerconsumption based on which parameters are being acquired and displayed.The MX-5 board can measure the Masimo SET parameters described aboveplus optional Rainbow® parameters including: hemoglobin (SpHb), oxygencontent (SpOC), carboxyhemoglobin (SpCO), methemoglobin (SpMet), andacoustic respiration rate (RRa) (among possibly others). The addition ofthe acoustic respiration rate can result in the display of thephysiological monitoring system 100 outputting a second waveform (e.g.,an acoustic respiration waveform).

The board 134 can include a signal processing system. Embodiments of thesignal processing system can employ a noise filtering system configuredto filter the data obtained during pulse oximetry measurements using redand infrared light, as such data is often contaminated due to motion.Identification and removal of these motion artifacts is often aprerequisite to any signal processing used to obtain blood oxygensaturation, pulse rate, or other physiological data. The signalprocessing system can provide the desired parameters as outputs for adisplay. Outputs for display are, for example, blood oxygen saturation,heart rate, and a clean plethysmographic waveform. Complex operationssuch as noise filtering and signal processing can require specializedprocessing or significant computational overhead, such that a typicaluser mobile device can not have sufficient processing power.Accordingly, the processing module 130 can perform signal processing onraw data received from the sensor and can provide physiologicalparameters as an output to a display and/or storage device.

The connection port 150 includes shell 151, bend relief 152, connector153, and cap 154. Bend relief 152 is an important feature of a medicalcable assembly for both the electrical and mechanical integrity andperformance of the second cable 140. The connection port 150 istypically rigid, and the bend relief 152 provides a transition from thestiffness of the connection port 150 to the flexibility of the secondcable 140. Preferably, bend relief 152 will prevent mechanical forceapplied to the exterior of the cable from being transferred to theelectrical terminations within the connector, which could lead tofailure.

Shell 151 generally encloses connector 153 and can be matable with cap154 to provide added protection for the connector 153. Connector 153 canbe shaped to physically and electrically connect with a specific device.Connection port 150 can be one of many different types of ports. Forexample, connection port 150 can be a device-specific port such as aniPhone port or another smartphone port, a USB port, an Ethernet port forconnection to a wired network, a serial port (e.g., RS232), a video outport which allows projection of the device screen on a larger display,combinations of the same, or the like. Further, the connection port 150can be equipped with one or more wireless interfaces (such as WiFi,Bluetooth, Zigbee, or the like).

FIG. 2 illustrates a block diagram of an example physiologicalmonitoring system 200. As illustrated, the system 200 includes a cable230 and a mobile device 220. The cable 230 includes a sensor 202, whichcan be any of the physiological sensors described above with respect toFIGS. 1A, 1B, and 1C, and a signal processing module 210. The mobiledevice 220 can provide power 206 to the signal processing module 210 andthe sensor 202. The sensor 210 can transmit raw data 204 to the signalprocessing module 210, and the signal processing module can convert theraw data 204 into data representing physiological parameters 226 fortransmission to the mobile device 220.

The mobile device 220 can be any of the portable computing devicesdiscussed above, such as a smartphone, laptop, tablet, or the like. Themobile device 220 can include a display 222 for display of theparameters, for example in a user interface and/or software application,as discussed in more detail below. The display 222 can include a displayscreen such as an LED or LCD screen, and can include touch sensitivetechnologies in combination with the display screen. The mobile device220 can also include storage 224, which can be configured for storage ofparameters 226 and parameter history data and/or software applicationsfor managing the data and sensor 110. In some embodiments, the storage224 can be physical storage of the device 220, and in some embodimentsthe storage 224 can be remote storage, such as on a server or servers ofa data hosting service. The mobile device 220 can also include a networkconnectivity feature 228 such as Bluetooth, satellite networkcapability, mobile communications capability, Wi-Fi, or the like. Insome embodiments the mobile device 220 can also include a data transferport.

The signal processing module 210 can be configured to receive raw sensordata 204 from the sensor 202, and to process the raw data 204 intoidentifiable parameters 226 for display and/or storage by the mobiledevice 220. In some embodiments, the mobile device 220 can not havesufficient processing power to handle the conversion of raw data 204 toidentifiable parameters 226. For example, in the context of pulseoximetry, the signal processing module 210 can use adaptive filtertechnology to separate an arterial signal, detected by a pulse oximetersensor, from the non-arterial noise (e.g. venous blood movement duringmotion). During routine patient motions (shivering, waving, tapping,etc.), the resulting noise can be quite substantial and can easilyoverwhelm a conventional ratio based oximetry system. This can provideaccurate blood oxygenation measurements even during patient motion, lowperfusion, intense ambient light, and electrocautery interference.Accordingly, false alarms can be substantially eliminated withoutsacrificing true alarms.

The signal processing module 210 can include a noise filter engine 212.In some embodiments, the noise filter engine 212 can perform a discretesaturation transform process to substantially remove noise from the rawsensor data 204. The discrete saturation transform process outputs amaximum power as an SpO₂ percentage. For example, the discretesaturation transform process can build a noise reference signal fromincoming red and infrared signals of a pulse oximeter sensor, in someembodiments, for each percent SpO₂, from 1 to 100 percent. The noisereference signal can be passed through an adaptive filter which cancancel correlated frequencies between the reference signal and theincoming infrared signal. If the frequencies between the two inputs areall similar, the entire signal can be canceled, and a low energy outputoccurs. If the frequencies between the two inputs are dissimilar, aminimal amount of signal cancels and a high-energy output can beobtained. The energy output from the adaptive filter can be measured andplotted for all possible saturations from 1 to 100 percent, for examplein 0.5 percent increments every 0.4 seconds, in some embodiments. Duringmeasurements in which the user exhibits no motion, a discrete cosinetransfer algorithm can generate one energy output peak, and severaloutput peaks can be generated during motion. Because arterial blood hasthe highest oxygen saturation, a peak picker process can select thehighest saturation peak as the percent SpO₂.

In some embodiments, the noise filter engine 212 can employ a pluralityof adaptive filter processes in parallel to separate the physiologicalsignal from the noise, and can leverage the unique strengths of eachadaptive filter processes to obtain accurate readings through variouspatient conditions. For example, in one embodiment of pulse oximetrymeasurements, parallel adaptive filters can include a discretesaturation transform, sinusoidal saturation transform, and fastsaturation transform, as well as possibly others. A sinusoidalsaturation transform can be a time domain transform that defines awindow around a derived pulse rate estimate, subtracts a preselected setof frequencies to find a minima, and can use the minima to determine thelocation of the maximum power and thus the true pulse rate. A fastsaturation transform may include, in some embodiments, a spectral orFourier transform, a spectral analysis, and identification ofphysiological parameters through frequency, magnitude, or other aspectsof the spectral analysis. In one embodiment, demodulation and decimationof the raw sensor data 204 may occur prior to the fast saturationtransform.

The noise filter engine 212 can optionally include an arbitration module214 in embodiments where multiple calculation engines are used. In someembodiments, the arbitration module 214 may be a confidence-basedarbitrator. The arbitration module 214 can include instructions tocompare the output of each adaptive filter process in order to generatea final determination of the denoised physiological signal. Thearbitration module 214 can also arbitrate physiological measurementsbased on any number of parameters, for example a highest confidencelevel or whether a threshold confidence level was reached. Furthermore,the arbitration module 214 can arbitrate based on expected values,previous values, averages or the like. Post processor 216 can applyadditional signal conditioning techniques to the output of thearbitration module 214 in order to output parameter data 226 to themobile device 220.

II. Example Computing Environment

FIG. 3 illustrates an embodiment of a computing environment 300 in whicha mobile patient monitoring device 330 can communicate with variouscomputing devices and services over a network 305. Although variousdevices and services are illustrated, in some embodiments the mobilepatient monitoring device 330 can be configured to communicate with asubset of the illustrated devices and services, and in some embodimentscan be configured to communicate with only one of the illustrateddevices and services.

In an embodiment, the mobile patient monitoring device 330 cancommunicate over a network 305 with calibration service 310 over thenetwork 305. The example network 305 shown can be a local area network(LAN), wide area network (WAN), the Internet, an intranet, cellularcommunications network, satellite communications network, orcombinations of the same or the like. The calibration service 310 canaccumulate and aggregate received physiological measurement data ascalibration data 314 to generate more accurate parameter values.Calibration data for physiological sensors such as pulse oximeters istypically calculated over a patient sample from a clinical study. Theclinically generated calibration data can be supplemented, in someembodiments, by the calibration data 314 gathered from physiologicalsensors 330. Advantageously, gathering measurement data from a number ofmobile physiological sensors 330 can expand such a data setsignificantly and lead to higher accuracies and/or new discoveriesregarding parameter measurement. The calibration data 314 can be storedanonymously or in other manners which are compliant with privacy lawsregarding medical data. In some embodiments, non-identifying demographicinformation can advantageously be associated with the calibration data314.

The calibration service 310 can include a calibration module 312configured with instructions to calculate a best fit function for thepopulation data 316 within the calibration data 314. The best fitfunction can be used to generate a calibration curve associating sensorreading values with parameter values. The best fit function can betransmitted to connected patient devices 330 in order to associatesensor readings with more accurate parameter values. Specifically, falsepositives can be reduced, variances in SpO₂ can be detected andfiltered, and/or measurement confidence can be evaluated, among otheradvantages. Calibration data 314 can also include individual data 318,for example individual variations from the expected sensor reading toparameter value relationship defined by the best fit function. Methodsof using a single sensor to improve calibration data which can beimplemented by the disclosed systems are disclosed in U.S. patentapplication Ser. No. 13/733,782, titled “AUTOMATED CCHD SCREENING ANDDETECTION,” filed Jan. 3, 2013, the entirety of which is herebyincorporated by reference.

In an embodiment, the mobile patient monitoring devices 330 cancommunicate with home/mobile clinician devices 320 over the network 305.Any type of clinician computing device 330 can communicate with mobilepatient monitoring device 330 including, for example, laptops, desktops,servers, work stations, tablets, wireless handheld devices such as cellphones, smart phones, personal digital assistants and wireless pagers,combinations of the same or the like. Alternatively or additionally, themobile patient monitoring devices 330 can communicate with patientdatabases of hospitals and other care facilities 225 over the network305. The mobile patient monitoring device 330 can output parameter data,trend data and/or alarms to the home/mobile clinician devices 320 and/orhospitals and other care facilities 225.

III. Example Software Applications

FIGS. 4A-4D illustrate various embodiments of applications for displayand management of physiological monitoring data. Such applications canbe available for download or installation on a user device from aprovider of the physiological sensors described herein, for example fromthe provider's web site, or through a mobile store application. In anembodiment, a mobile physiological monitoring software application canbe initialized when a user connects a sensor cable to their mobiledevice. The user interface examples illustrated in FIGS. 4A-4D areprovided to illustrate and not to limit the capabilities of suchapplications.

Some embodiments of the software application can be used with thesmartphone 160 of FIGS. 1A, 1B, and 1C, though any mobile user devicecan be used in other embodiments. As illustrated in FIG. 4A, smartphone160 includes a display 410, which can be used to generate a userinterface for the software application. The application can include aplurality of display portions in which a plurality of physiologicalparameters can be displayed, such as SpO₂ display 420, heart ratedisplay 430, perfusion index display 450, or plethysmographic waveformdisplay 450. Any combination of the physiological parameters disclosedherein can be displayed on the smartphone 160. The configuration ofthese various display portions is meant for illustrative purposes, andone skilled in the art would appreciate that the parameter displayscould be rearranged relative to one another, displayed alone, or theuser interface could be modified to include other parameter displayportions. Another example of a variety of display portions isillustrated in FIG. 4B. Further, although some of the parameter displayportions employ numerical representations of the physiological data,some embodiments can employ graphical representations, for example abeating heart can indicate heart rate.

The user interface can also include an options display portion 460 whichallows the user to interact with his physiological monitoring data in avariety of ways. For example, the user can choose to view trends in thedata, as illustrated in FIG. 4C, or to change the manner in which thedata is represented such as by viewing a histogram or other graph. Theuser can be also able to view the history of his physiologicalmeasurement data. In some embodiments, history or trend data can bedisplayed with a start date and/or time and an end date and/or time, andthe user can be able to adjust the window of data displayed. Forexample, on a touch sensitive interface the user can narrow or expand awindow of trend data using a pinch gesture with two fingers. The usercan also be able to export a selected amount of trend or history data,such as by electronic mail, through a medical service, or as aspreadsheet, to name a few examples. A settings option can be displayedwhich would allow the user to modify other aspects of the program, andcan also enable the user to set alarms or reminders to take futuremeasurements.

Turning to FIG. 4D, an example instruction user interface is shown whichcan be presented to a user upon initialization of the application. Theinstruction interface can include graphical and numbered steps to guidethe user through set up of the sensor, and can include a user selectableoption to start tracking physiological parameter measurements.

In certain embodiments, the application can be downloadable from acomputer network at a cost, by subscription, pay-per-use, or the like.Other embodiments can advantageously incorporated caregiver-specificapplications which include reminders for timed measurements orprotocols. For example, a caregiver for a pre-surgical patient candesire measurement data for a certain minimum time per minimum period(20 min per every hour) or the like to have sufficient data to makediagnosis or decisions for treatment. A caregiver-specific applicationcan be advantageously programmed to accomplish such a protocol.Moreover, signal quality or confidence indicators such as perfusionindex (“PI”) or signal IQ (“SIQ”) can be used to ensure data meetscertain minimum confidence and/or signal-to-noise limitations. Thus, theapplication can implement the protocol and extend or add measurementintervals to ensure minimum signal quality standards are met. Othercaregiver-specific applications can provide animated or textualinstructions, links to online information regarding certain monitoringsituations, ailments, or other useful patient research.

In an embodiment, data acquired through the application can be uploadedto caregiver or device provider systems to increase the population dataand used to improve signal processing. In a preferred embodiment, issuesof privacy and compliance with governmental regulations are strictlyenforced through the application logic. In some embodiments,non-identifying demographic information can advantageously be associatedwith such data. Moreover, password and/or additional authenticationrequirements can be required to access stored data in the application,such as, for example, fingerprint technologies, facial recognitiontechnologies employing the smartphone's camera, voice recognitiontechnologies employing the smartphone's audio transducer, or the likecan further assist in meeting privacy concerns.

IV. Overview of Compatible Sensor Embodiments

As illustrated in FIG. 5A, a physiological sensor 520 can be anelectroencephalograph (“EEG”) configured for measurement of electricalactivity along the scalp. Such mobile EEG systems can be used, forexample, in detecting and monitoring epileptic activity. EEG systems canalso be used for diagnosis and management of sleep disorders or forstudies of sleep. Electroencephalography is used extensively inneuroscience, cognitive science, cognitive psychology, neurolinguisticsand psychophysiological research. In many of these contexts, a sensor520 compatible with a common mobile computing device of a user wouldprovide advantages such as convenience and affordability. In someembodiments, the sensor 520 can be SEDLine®, available from Masimo.SEDLine® brain function monitoring can use four channels of information,in some embodiments, to monitor both sides of the brain's electricalactivity.

Turning to FIG. 5B, a capnometer or capnograph 530 can be configured formobile physiological parameter measurement. Such sensors 530 can bedesigned for the measurement of CO2, N2O, and anesthetic agents, amongothers. Capnography can be useful for metabolic measurements andnutritional assessment, and accordingly a mobile sensor 530 can provideincreased accessibility for such uses.

An acoustic respiratory monitor 540, as shown in FIG. 5C, can also beconfigured for mobile physiological parameter measurement. An acousticrespiratory monitor 540 can measure respiration rate using an adhesivesensor with an integrated acoustic transducer that can be comfortablyapplied to the patient's neck. Continuous monitoring of respiration ratecan be important for post-surgical patients receiving patient-controlledanalgesia for pain management, as the sedation can induce respiratorydepression and place patients at considerable risk of serious injury ordeath. Accordingly, a mobile respiratory monitor 540 can be desirablefor convenient and continuous monitoring of such patients, among otherreasons.

V. Overview of Example Mobile Physiological Monitoring Processes

FIG. 6 illustrates an embodiment of a pre-anesthesia monitoring process600. The process can be implemented by the physiological monitoringsystem 100 of FIGS. 1A, 1B, and 1C, in some embodiments.

The process 600 can begin at block 605 in which a care providerrecommends a medical procedure requiring anesthesia for a patient.Certain medical conditions can present safety concerns for the patientduring anesthesia, so at block 610 the patient can be provided with aportable monitoring system including a sensor connectable to one of thepatient's personal mobile computing devices. In some embodiments thepatient can be provided with multiple sensors and/or a softwareapplication for collection and management of physiological data.

At block 615, the portable monitoring system can collect and storephysiological data from the patient. Optionally, at block 620, thecollected data is analyzed for risk factors indicating a medicalcondition with implications for anesthesia, such as obstructive sleepapnea. At block 625, the collected data and/or analysis of the data isprovided to the patient's physician or another care provider. In someembodiments, a physician can conduct the analysis after receiving thepatient's data.

At decision block 630, a determination is made regarding whether thedata analysis indicates that sleep apnea or another medical conditionimpacting the safety of anesthesia is present. If such a condition ispresent in the data, then the process 600 moves to block 645 in whichthe anesthesiologist is alerted. At step 650, a patient treatmentstrategy is developed that addresses the possible complications of thepatient undergoing anesthesia with the detected condition. If nosafety-impairing medical condition is present in the data, then theprocess 600 moves to block 640 in which the patient's physician canelect to proceed with the recommended medical procedure and anesthesia.

FIG. 7 illustrates an embodiment of a continuum of care process 700. Theprocess 700 can be implemented, in some embodiments, by the computingenvironment 300 of FIG. 3. In an embodiment, the process 600 can beimplemented at least in part by the network 305 to facilitate continuedpatient monitoring when a patient leaves a hospital or other facility.

At block 705, monitoring data of a patient is received at a clinicalfacility, for example by a networked medical service which can receiveand store patient monitoring data, among other features. Once thepatient is discharged, at block 710 the patient can be outfitted with aportable monitoring system. The portable monitoring system can monitorthe same parameters as a device used to monitor the patient in theclinical facility. In addition, the portable monitoring system may, forinstance, be any of the sensors and processing cable components, orvariations thereof, described herein.

When a patient is discharged, there is a typically a period of timewhere the patient is not being monitored once the patient leaves thefacility. However, the continuum of care process 700 employing mobilephysiological sensors can facilitate continued monitoring of thepatient, for example during travel between the facility and thepatient's residence or when the patient arrives at home, by receivingmonitoring data from the patient via a cellular or satellite network atblock 715. An activity level of the patient, for example resting orwalking, can be monitored at block 620 in order to set the appropriatethresholds for determining when physiological parameters indicating analarm condition are occurring at block 725. The patient's activity levelcan be monitored by the device, in some embodiments, or can be input bythe patient or a care giver.

Periodically, the mobile physiological sensor system can recheck thepatient's activity level at block 730 to determine whether the activitylevel has changed. If the patient's activity level has changed, then theprocess 700 loops back to block 725 to adjust alarm settings for thepatient's physiological data based on the activity level. If thepatient's activity level has not changed, then the process 700 can moveto block 735 in which it is determined whether an alarm condition isoccurring based on the patient's physiological parameters and the alarmsettings. A software application installed on the patient's mobiledevice can be configured to detect the alarm condition. If an alarmcondition is not occurring, then the process 700 loops back to block 715in which the mobile physiological sensor continues to performphysiological measurements and transmit the measurements to the mobiledevice through a signal conditioning processor. If an alarm condition isdetected at block 735, then the patient's mobile device can pass anotification to a care provider via a network connection. Accordingly,the mobile physiological sensor system can facilitate a continuum ofcare for a patient and continuous monitoring even when a patient hasleft a clinical facility.

FIG. 8 illustrates an embodiment of a mobile physiological datamonitoring process 800. The process can be implemented, in someembodiments, by the physiological monitoring system 100 of FIGS. 1A, 1B,and 1C, or the physiological monitoring system 200 of FIG. 2.

At block 805, a portable user monitoring system is provided includingphysiological sensor, processing module, and device connection port. Thephysiological sensor can be any of the sensor examples discussed herein.The processing module can be the processing module 130 described inFIGS. 1A, 1B, and 1C or the signal processing module 210 of FIG. 2. Theprocessing module can implement Masimo SET technology, in someembodiments. The device connection port can be configured for use with astandard personal computing device, such as a smartphone, and can beconnected to the processing module physically via a cable or wirelessly.

At block 810, the user's mobile computing device, while connected to theportable patient monitoring system, provides power to the sensor andprocessing module. Accordingly, the sensor and processing module can beconfigured in some embodiments so as to draw only minimal power from themobile computing device, as such devices are typically powered bybatteries.

At block 815, the processing module receives raw physiological sensordata from the sensor. The processing module performs signal conditioningon the raw data at block 820, for example any of the signal conditioningtechniques described herein, to remove noise from the raw data andobtain physiological parameter data. At block 825, the processing moduleoutputs the physiological parameter data to the user's mobile computingdevice for display and/or storage on the device. Accordingly, a user canconveniently conduct physiological measurements and be presented withphysiological data on their mobile device in a wide variety of contexts.

FIG. 9 illustrates an embodiment of a user-guided monitoring process900, which can be carried out by a user on their personal computingdevice without the need for physician or caregiver aid. The process 900can be carried out by a mobile physiological monitoring application, asdiscussed above, in conjunction with a mobile physiological sensor. Thephysiological sensor can be any of the sensor examples discussed herein.

At block 905, the user is instructed to insert the connection port of acable including a physiological sensor and a processor into acorresponding port on their mobile computing device, and at block 910the user is instructed to place the sensor at a measurement site. Insome embodiments, these blocks can be implemented by an instruction userinterface such as is depicted in FIG. 4D and discussed above.

At block 915, the mobile device receives measurement data, which can beraw sensor data that has been processed by a processing module prior tobeing sent to the mobile device. At block 920, the mobile physiologicalmonitoring application can determine based on the measurement datawhether an error is occurring. If it is determined that an error is notoccurring, then the mobile device can continue to receive measurementdata at block 915. If it is determined that an error is occurring, thenthe mobile physiological monitoring application can determine apotential or likely error source at block 925.

Based on the determined error source, the mobile physiologicalmonitoring application may, at block 930, display a message to aid theuser to aid in resolution of the error. Example messages include “Ensurecable is connected,” “Sensor not working.” “Place sensor on properly,”“Searching for pulse,” “Interference detected, see manual,” “Lowperfusion, see manual,” “Too much surrounding light,” “Low signalquality, see manual,” and “Connecting, please wait,” among others. Insome embodiments an audible or visual indication can also be provided toalert the user to the presence of the error. At block 935, the mobilephysiological monitoring application can determine whether the user hasresolved the error. The mobile physiological monitoring application canrepeat this action at predetermined intervals until the error isresolved or the application is terminated by the user, in someembodiments. In other embodiments, the mobile physiological monitoringapplication can determine whether the error has been resolved based on achange in received measurement data values. If, after a predeterminedthreshold of time, the error is not resolved, then the process 900 ends.If the error is resolved, the process 900 loops back to block 915, andthe mobile device can continue to receive measurement data.

FIG. 10 illustrates an embodiment of a data-logging process 1000. Thedata-logging process 1000 can run continuously or periodically duringoperation of a mobile physiological monitoring application, as discussedabove.

At block 1005, the mobile physiological monitoring application canreceive measurement data, which can be raw sensor data that has beenprocessed by a processing module prior to being sent to a mobile device.This data is stored, at block 101, in a user history, for example instorage of the mobile device or in a networked data storage service. Atblock 1015, the mobile physiological monitoring application determinesthat a user has requested to be presented with history data, andaccordingly outputs at least some of the stored data for display to theuser at block 1020. In some embodiments, the user can specify a desiredrange of stored history data when making the request. In otherembodiments, the device can output a predetermined range of the historydata, for example based on a recent time window of the data or patternsin the data.

At block 1025, the mobile physiological monitoring application candynamically adjust the amount of displayed data based on user input.This step can be optional based on whether a user provides inputregarding adjusting the data. In some embodiments, the user can be ableto specify particular physiological parameters to add or remove from thedisplay. In an embodiment implemented on a touch-sensitive display, auser can use a two-finger pinching gesture to change the range of thetime window of the data, or can use a swiping motion to move forwards orbackwards through the data. Such adjustments can be implemented usingother user interface elements on non-touch sensitive displays. A usercan also be able to select from a variety of possible representations ofthe data, such as a chart, graph, plot, or other graphicalrepresentation as well as numerical representations such asspreadsheets, in some embodiments.

At block 1030, the mobile physiological monitoring application canreceive a user request to export the stored history data. If no suchrequest is received, then the mobile physiological monitoringapplication can loop back to block 1005 and continue to receivephysiological measurement data. If the user requests to export the data,then at block 1035 the mobile physiological monitoring application canexport a subset of the stored history data according to user formatspecification. For example, the user can specify a time and/or daterange of data to export, can select a format (such as a spreadsheet or agraph), and can select an exporting means such as email or directtransmission to a physician or networked medical service.

At block 1040, the user can be presented with an option to delete thestored history data. In some embodiments, the user can be asked whetherto delete data that has been exported. If the user does not want todelete the data, then the mobile physiological monitoring applicationcan loop back to block 1005 and continue to receive physiologicalmeasurement data. If the user requests to delete the data, then themobile physiological monitoring application can clear stored historydata according to user instructions, and can then loop back to block1005 and continue to receive physiological measurement data.

VI. Terminology

Although many of the examples discussed herein are in the context ofpulse oximetry, this is for illustrative purposes only. The sensors,signal conditioning techniques, and mobile applications discussed hereincan be adapted for other physiological parameters or for multiplephysiological parameters.

Many other variations than those described herein will be apparent fromthis disclosure. For example, depending on the embodiment, certain acts,events, or functions of any of the algorithms described herein can beperformed in a different sequence, can be added, merged, or left out alltogether (e.g., not all described acts or events are necessary for thepractice of the algorithms). Moreover, in certain embodiments, acts orevents can be performed concurrently, e.g., through multi-threadedprocessing, interrupt processing, or multiple processors or processorcores or on other parallel architectures, rather than sequentially. Inaddition, different tasks or processes can be performed by differentmachines and/or computing systems that can function together.

The various illustrative logical blocks, modules, and algorithm stepsdescribed in connection with the embodiments disclosed herein can beimplemented as electronic hardware, computer software, or combinationsof both. To clearly illustrate this interchangeability of hardware andsoftware, various illustrative components, blocks, modules, and stepshave been described above generally in terms of their functionality.Whether such functionality is implemented as hardware or softwaredepends upon the particular application and design constraints imposedon the overall system. The described functionality can be implemented invarying ways for each particular application, but such implementationdecisions should not be interpreted as causing a departure from thescope of the disclosure.

The various illustrative logical blocks and modules described inconnection with the embodiments disclosed herein can be implemented orperformed by a machine, such as a general purpose processor, a digitalsignal processor (DSP), an application specific integrated circuit(ASIC), a field programmable gate array (FPGA) or other programmablelogic device, discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. A general purpose processor can be a microprocessor,but in the alternative, the processor can be a controller,microcontroller, or state machine, combinations of the same, or thelike. A processor can also be implemented as a combination of computingdevices, e.g., a combination of a DSP and a microprocessor, a pluralityof microprocessors, one or more microprocessors in conjunction with aDSP core, or any other such configuration. Although described hereinprimarily with respect to digital technology, a processor can alsoinclude primarily analog components. For example, any of the signalprocessing algorithms described herein can be implemented in analogcircuitry. A computing environment can include any type of computersystem, including, but not limited to, a computer system based on amicroprocessor, a mainframe computer, a digital signal processor, aportable computing device, a personal organizer, a device controller,and a computational engine within an appliance, to name a few.

The steps of a method, process, or algorithm described in connectionwith the embodiments disclosed herein can be embodied directly inhardware, in a software module executed by a processor, or in acombination of the two. A software module can reside in RAM memory,flash memory, ROM memory, EPROM memory, EEPROM memory, registers, harddisk, a removable disk, a CD-ROM, or any other form of non-transitorycomputer-readable storage medium, media, or physical computer storageknown in the art. An exemplary storage medium can be coupled to theprocessor such that the processor can read information from, and writeinformation to, the storage medium. In the alternative, the storagemedium can be integral to the processor. The processor and the storagemedium can reside in an ASIC. The ASIC can reside in a user terminal. Inthe alternative, the processor and the storage medium can reside asdiscrete components in a user terminal.

Conditional language used herein, such as, among others, “can,” “might,”“may,” “e.g.,” and the like, unless specifically stated otherwise, orotherwise understood within the context as used, is generally intendedto convey that certain embodiments include, while other embodiments donot include, certain features, elements and/or states. Thus, suchconditional language is not generally intended to imply that features,elements and/or states are in any way required for one or moreembodiments or that one or more embodiments necessarily include logicfor deciding, with or without author input or prompting, whether thesefeatures, elements and/or states are included or are to be performed inany particular embodiment. The terms “comprising,” “including,”“having,” and the like are synonymous and are used inclusively, in anopen-ended fashion, and do not exclude additional elements, features,acts, operations, and so forth. Also, the term “or” is used in itsinclusive sense (and not in its exclusive sense) so that when used, forexample, to connect a list of elements, the term “or” means one, some,or all of the elements in the list.

While the above detailed description has shown, described, and pointedout novel features as applied to various embodiments, it will beunderstood that various omissions, substitutions, and changes in theform and details of the devices or algorithms illustrated can be madewithout departing from the spirit of the disclosure. As will berecognized, certain embodiments of the inventions described herein canbe embodied within a form that does not provide all of the features andbenefits set forth herein, as some features can be used or practicedseparately from others.

What is claimed is:
 1. A physiological monitoring system comprising: aphysiological sensor configured to monitor one or more physiologicalparameters of a patient; a mobile computing device comprising a display;a cable including: a processing board configured to establish anelectrical signal connection between the physiological sensor and themobile computing device, wherein the processing board is furtherconfigured to receive raw data representing the monitored one or morephysiological parameters and to perform signal processing to providefiltered parameter data to the mobile computing device, a first portionof the cable coupled between the physiological sensor and the processingboard, the first portion of the cable extending a first distancemechanically isolating the processing board from the physiologicalsensor, and a second portion of the cable coupled between the processingboard and the mobile computing device, the second portion of the cableextending a second distance, wherein the second distance is smaller thanthe first distance; and an enclosure comprising: a body portionsurrounding the processing board, a first bend relief on a first side ofthe body portion, and a second bend relief on a second side of the bodyportion, the first portion of the cable coupled between thephysiological sensor and the processing board through the first bendrelief and the second portion of the cable coupled between theprocessing board and the mobile computing device through the second bendrelief.
 2. The physiological monitoring system of claim 1, wherein theprocessing board is coupled to a port using a second portion of thecable, wherein the port is connectable to the mobile computing device.3. The physiological monitoring system of claim 1, wherein theprocessing board comprises a digital processing board and an analogprocessing board.
 4. The physiological monitoring system of claim 1,wherein the processing board is in communication with an informationelement.
 5. The physiological monitoring system of claim 1, wherein themobile computing device provides a power signal to the processing boardand the physiological sensor.
 6. The physiological monitoring system ofclaim 1, wherein the mobile computing device presents the filteredparameter data to a user on the display.
 7. The physiological monitoringsystem of claim 1, wherein the mobile computing device further comprisesstorage configured to store the filtered parameter data.
 8. Thephysiological monitoring system of claim 1, wherein the mobile computingdevice is configured to transmit, via a network connection, the filteredparameter data to at least one of a calibration service, a physiciancomputing device, or a medical facility patient database.
 9. Thephysiological monitoring system of claim 1, wherein the physiologicalsensor comprises one or more of a pulse oximeter, capnometer,capnograph, acoustic respiratory sensor, electroencephalograph,electrocardiograph, or temperature sensor.
 10. A computer-implementedmethod of mobile physiological monitoring, the method comprising:providing a portable physiological monitoring system comprising: aphysiological sensor configured to monitor one or more physiologicalparameters of a patient, a cable including: a processing boardconfigured to establish an electrical signal connection between thesensor and a mobile computing device, a first portion of the cablecoupled between the physiological sensor and the processing board, thefirst portion of the cable extending a first distance mechanicallyisolating the processing board from the physiological sensor, and asecond portion of the cable coupled between the processing board and themobile computing device, the second portion of the cable extending asecond distance, wherein the second distance is smaller than the firstdistance, and an enclosure comprising: a body portion surrounding theprocessing board, a first bend relief on a first side of the bodyportion, and a second bend relief on a second side of the body portion,the first portion of the cable coupled between the physiological sensorand the processing board through the first bend relief and the secondportion of the cable coupled between the processing board and the mobilecomputing device through the second bend relief; generating raw datarepresenting the monitored one or more physiological parameters usingthe physiological sensor; receiving the raw data at the processing boardvia the first portion of the cable; performing signal processing on theraw data using the processing board, wherein the signal processinggenerates filtered parameter data; and transmitting the filteredparameter data via the second portion of the cable to the mobilecomputing device.
 11. The computer-implemented method of claim 10,wherein the signal processing comprises: determining a noise signalpresent in the raw data; filtering the raw data to remove the noisesignal; and outputting the filtered raw data as filtered parameter data.12. The computer-implemented method of claim 10, wherein the filteredparameter data comprises one or more of oxygen saturation, pulse rate,perfusion index, signal quality, oxygen content, carboxyhemoglobin,methemoglobin, and acoustic respiration rate.
 13. Thecomputer-implemented method of claim 10, wherein transmitting thefiltered parameter data to the mobile computing device comprisestransmitting the filtered parameter data through the cable and aconnection port to the mobile computing device.
 14. Thecomputer-implemented method of claim 10, wherein transmitting thefiltered parameter data to the mobile computing device comprisestransmitting the filtered parameter data through the cable to a wirelesscommunication module, wherein the filtered parameter data is transmittedwirelessly to the mobile computing device from the wirelesscommunication module.
 15. The computer-implemented method of claim 10,further comprising providing a mobile monitoring application configuredto track and display the filtered parameter data.
 16. Thecomputer-implemented method of claim 15, wherein the mobile monitoringapplication is further configured to output stored history datarepresenting filtered parameter data monitored over a period of time.17. The computer-implemented method of claim 16, wherein outputtingstored history data comprises presenting the stored history data to auser on a display of the mobile computing device.
 18. Thecomputer-implemented method of claim 16, wherein outputting storedhistory data comprises exporting the stored history data to anothercomputing device.
 19. The computer-implemented method of claim 10,further comprising: connecting to a network using the mobile computingdevice; and transmitting the filtered parameter data over the network.20. The computer-implemented method of claim 19, wherein the filteredparameter data is transmitted over the network to a calibration service,the method further comprising incorporating the filtered parameter datainto a calibration data set.
 21. The computer-implemented method ofclaim 20, further comprising using the calibration data set to generatea calibration curve.